Papers with lexicalization

2 papers
An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)

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Challenge: Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics.
Approach: They propose to standardize experimental settings for better comparability between methods . they compare existing methods with those proposed by decade-old models .
Outcome: The proposed methods perform better than decade-old models on English and Japanese, respectively, compared with decade- old models.
Second-Order Unsupervised Neural Dependency Parsing (2020.coling-main)

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Challenge: supervised dependency parsers can reach a very high accuracy, but they require treebanks for training.
Approach: They propose a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information.
Outcome: The proposed model achieves 10% improvement over the previous state-of-the-art model on the full WSJ dataset.

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